Deep Compositional Spatial Models

DOI10.1080/01621459.2021.1887741zbMath1515.62090arXiv1906.02840OpenAlexW3131234345MaRDI QIDQ6110701

Quan Vu, Tin Lok James Ng, Maurizio Filippone, Andrew Zammit-Mangion

Publication date: 6 July 2023

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1906.02840

zbMATH Keywords

spatial statisticsstochastic processesnonstationarityvariational Bayesdeep models

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Artificial neural networks and deep learning (68T07)

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Modeling Nonstationary and Asymmetric Multivariate Spatial Covariances via Deformations, A Review of Data‐Driven Discovery for Dynamic Systems, Vecchia-approximated Deep Gaussian Processes for Computer Experiments, Discussion on competition on spatial statistics for large datasets

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